#!/bin/bash

export PYTHONWARNINGS="ignore:semaphore_tracker:UserWarning"
################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size RANK_SIZE
# 网络名称，同目录名称
Network="BigGAN"
experiment_name="biggan_performance"
# 训练batch_size
batch_size=32
accumulations=$((256/${batch_size}))
echo "batch_size: ${batch_size},accumulations: ${accumulations},total: $((${batch_size}*${accumulations}))"
# 训练使用的npu卡数
export RANK_SIZE=1
# 数据集路径,保持为空,不需要修改
data_path=""
# 训练epoch
train_epochs=4
# 加载数据进程数
workers=16
# device id
device_id=0

# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --device_id* ]];then
        device_id=`echo ${para#*=}`
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    fi
done

# 校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be config"
    exit 1
fi
# 校验是否指定了device_id,分动态分配device_id与手动指定device_id,此处不需要修改
if [ $ASCEND_DEVICE_ID ];then
    echo "device id is ${ASCEND_DEVICE_ID}"
elif [ ${device_id} ];then
    export ASCEND_DEVICE_ID=${device_id}
    echo "device id is ${ASCEND_DEVICE_ID}"
else
    "[Error] device id must be config"
    exit 1
fi



###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi
##################在脚本中创建目录，防止多进程冲突##############
if [[ -d "logs" ]];then
  rm -rf logs
  mkdir logs
else
  mkdir logs
fi

if [[ -d "weights" ]];then
  rm -rf weights
  mkdir weights
else
  mkdir weights
fi

if [[ -d "samples" ]];then
  rm -rf samples
  mkdir samples
else
  mkdir samples
fi

#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi


python3 -u train.py \
  --data_root=${data_path} \
  --dataset I128_hdf5 --num_workers ${workers} --batch_size ${batch_size} \
  --num_G_accumulations ${accumulations} --num_D_accumulations ${accumulations} \
  --num_D_steps 1 --G_lr 1e-5 --D_lr 4e-5 --D_B2 0.999 --G_B2 0.999 \
  --num_epochs ${train_epochs} \
  --G_attn 64 --D_attn 64 \
  --G_nl inplace_relu --D_nl inplace_relu \
  --SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \
  --G_ortho 0.0 \
  --G_shared \
  --G_init ortho --D_init ortho \
  --hier --dim_z 120 --shared_dim 128 \
  --G_eval_mode \
  --G_ch 96 --D_ch 96 \
  --save_every 500 --num_save_copies 0 --seed 0 \
  --addr=$(hostname -I | awk '{print $1}') \
  --dist-url='tcp://127.0.0.1:50000' \
  --dist-backend 'hccl' \
  --world-size=1 \
  --rank 0 \
  --device='npu' \
  --device_list "0" \
  --experiment_name ${experiment_name} \
  --stop-iter 10 \
  >> ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 
wait

#################获取训练数据################
#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPS=`grep -a "FPS" ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk 'END{print}' | awk -F ":" '{print $NF}'`
FPS=${FPS:2}
FPS=$(echo $FPS)
TrainAccuracy="no accuracy"
#打印，不需要修改
echo "Final Performance images/sec : $FPS"
#打印，不需要修改
echo "E2E Training Duration sec : $e2e_time"
#打印，不需要修改
echo "TrainAccuracy : $TrainAccuracy"

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'

#获取性能数据，不需要修改
#吞吐量
ActualFPS=${FPS}
#单迭代训练时长
TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep Epoch: ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log \
       | awk -F "," '{print $4","$5","$6}' \
       > ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}'  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${TrainAccuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log

# save train log
if [ -d "${test_path_dir}/output/${ASCEND_DEVICE_ID}/" ];then
  cp -rf "${test_path_dir}/output/${ASCEND_DEVICE_ID}/" "./logs"
else
  echo "log directory don't exsit"
  exit 1
fi